Corpus is a kind of important resource for knowledge acquisition in the natural language processing （NLP）. However, up to now, in the biomedical domain comparatively fewer corpus focus on semantic association among ...Corpus is a kind of important resource for knowledge acquisition in the natural language processing （NLP）. However, up to now, in the biomedical domain comparatively fewer corpus focus on semantic association among all tokens in a sentence. We proposed an annotation scheme based on feature structure theory for enriching biomedical domain corpora with token semantic association （TSA）. There are 227 documents of the BioNLP GE ST training data annotated to form TSA corpus in which each annotated item shows a token semantic association that appears as a triple. The annotation of token semantic association has the potential to significantly advance biomedical text mining by providing rich token semantic information for NLP systems especially for the sophisticated IE systems, such as bio-event extraction.展开更多
Remote sensing Change Detection(CD)involves identifying changing regions of interest in bi-temporal remote sensing images.CD technology has rapidly developed in recent years through the powerful learning ability of Co...Remote sensing Change Detection(CD)involves identifying changing regions of interest in bi-temporal remote sensing images.CD technology has rapidly developed in recent years through the powerful learning ability of Convolutional Neural Networks(CNN),affording complex feature extraction.However,the local receptive fields in the CNN limit modeling long-range contextual relationships in semantic changes.Therefore,this work explores the great potential of Siamese Transformers in CD tasks and proposes a general CD model entitled STCD that relies on Swin Transformers.In the encoding process,pure Transformers without CNN are used to model the long-range context of semantic tokens,reducing computational overhead and improving model efficiency compared to current methods.During the decoding process,the 3D convolution block obtains the changing features in the time series and generates the predicted change map in the deconvolution layer with axial attention.Extensive experiments on three binary CD datasets and one semantic CD dataset demonstrate that the proposed STCD model outperforms several popular benchmark methods considering performance and the required parameters.Among the STCD variants,the F1-Score of the Base-STCD on the three binary CD datasets LEVIR,DSIFN,and SVCD reached 89.85%,54.72%,and 93.75%,respectively,and the mF1-Score and mIoU on the semantic CD dataset SECOND were 75.60%and 66.19%.展开更多
基金Supported by the National Natural Science Foundation of China(61202304,61173095,61173062,61202193)
文摘Corpus is a kind of important resource for knowledge acquisition in the natural language processing （NLP）. However, up to now, in the biomedical domain comparatively fewer corpus focus on semantic association among all tokens in a sentence. We proposed an annotation scheme based on feature structure theory for enriching biomedical domain corpora with token semantic association （TSA）. There are 227 documents of the BioNLP GE ST training data annotated to form TSA corpus in which each annotated item shows a token semantic association that appears as a triple. The annotation of token semantic association has the potential to significantly advance biomedical text mining by providing rich token semantic information for NLP systems especially for the sophisticated IE systems, such as bio-event extraction.
基金supported by the Military Commission Science and Technology Committee Leading Fund[grant number 18-163-00-TS-004-080-01].
文摘Remote sensing Change Detection(CD)involves identifying changing regions of interest in bi-temporal remote sensing images.CD technology has rapidly developed in recent years through the powerful learning ability of Convolutional Neural Networks(CNN),affording complex feature extraction.However,the local receptive fields in the CNN limit modeling long-range contextual relationships in semantic changes.Therefore,this work explores the great potential of Siamese Transformers in CD tasks and proposes a general CD model entitled STCD that relies on Swin Transformers.In the encoding process,pure Transformers without CNN are used to model the long-range context of semantic tokens,reducing computational overhead and improving model efficiency compared to current methods.During the decoding process,the 3D convolution block obtains the changing features in the time series and generates the predicted change map in the deconvolution layer with axial attention.Extensive experiments on three binary CD datasets and one semantic CD dataset demonstrate that the proposed STCD model outperforms several popular benchmark methods considering performance and the required parameters.Among the STCD variants,the F1-Score of the Base-STCD on the three binary CD datasets LEVIR,DSIFN,and SVCD reached 89.85%,54.72%,and 93.75%,respectively,and the mF1-Score and mIoU on the semantic CD dataset SECOND were 75.60%and 66.19%.
